Biology subjects

Quarteroni, A.

Publications and source records attributed to Quarteroni, A..

3 recordsLinked to original sources

bioRxiv2024

A comprehensive stroke risk assessment by combining atrial computational fluid dynamics simulations and functional patient data

Stroke, a major global health concern often rooted in cardiac dynamics, demands precise risk evaluation for targeted intervention. Current risk models, like the CHA2DS2-VASc score, often lack the granularity required for personalized predictions. In this study, we present a nuanced and thorough stroke risk assessment by integrating functional insights from cardiac magnetic resonance (CMR) with patient-specific computational fluid dynamics (CFD) simulations. Our cohort, evenly split between control and stroke groups, comprises eight patients. Utilizing CINE CMR, we compute kinematic features, revealing smaller left atrial volumes for stroke patients. The incorporation of patient-specific atrial displacement into our hemodynamic simulations unveils the influence of atrial compliance on the flow fields, emphasizing the importance of LA motion in CFD simulations and challenging the conventional rigid wall assumption in hemodynamics models. Standardizing hemodynamic features with functional metrics enhances the differentiation between stroke and control cases. While standalone assessments provide limited clarity, the synergistic fusion of CMR-derived functional data and patient-informed CFD simulations offers a personalized and mechanistic understanding, distinctly segregating stroke from control cases. Specifically, our investigation reveals a crucial clinical insight: normalizing hemodynamic features based on ejection fraction fails to differentiate between stroke and control patients. Differently, when normalized with stroke volume, a clear and clinically significant distinction emerges and this holds true for both the left atrium and its appendage, providing valuable implications for precise stroke risk assessment in clinical settings. This work introduces a novel framework for seamlessly integrating hemodynamic and functional metrics, laying the groundwork for improved predictive models, and highlighting the significance of motion-informed, personalized risk assessments.

bioengineering↗

bioRxiv2023

Accurate and Efficient 3D Reconstruction of Right Heart Shape and Motion from Multi-Series Cine-MRI

The accurate reconstruction of the right heart geometry and motion from time-resolved medical images enhances diagnostic tools based on image visualization as well as the analysis of cardiac blood dynamics through computational methods. Due to the peculiarity of the right heart morphology and motion, commonly used segmentation and/or reconstruction techniques, which only employ Short-Axis cine-MRI, lack accuracy in relevant regions of the right heart, like the ventricular base and the outflow tract. Moreover, the reconstruction procedure is time-consuming and, in the case of the generation of computational domains, requires a lot of manual intervention. This paper presents a new method for the accurate and efficient reconstruction of the right heart geometry and motion from time-resolved MRI. In particular, the proposed method makes use of surface morphing to merge information coming from multi-series cine-MRI (such as Short/Long-Axis and 2/3/4 Chambers acquisitions) and to reconstruct important cardiac features. It also automatically provides the complete cardiac contraction and relaxation motion by exploiting a suitable image registration technique. The method is applied both to a healthy and a pathological (tetralogy of Fallot) case, and yelds more accurate results than standard procedures. The proposed method is also employed to provide significant input for computational fluid dynamics. The corresponding numerical results demonstrate the reliability of our approach in the computation of clinically relevant blood dynamics quantities.

bioengineering↗

bioRxiv2020

Modeling the effect of COVID-19 disease on the cardiac function: a computational study

BackgroundThe effect of COVID-19 on the cardiac function and on the vascular system increases the morbidity and mortality of infected subjects with cardiovascular diseases. ObjectivesTo provide preliminary results on cardiac global outcomes (such as cardiac output, ventricular pressures) obtained by means of computational models in plausible scenarios characterized by COVID-19. MethodsWe considered a lumped parameters computational model of the cardiovascular system, which models, from the mechanical point of view, the systemic and pulmonary circulations, the four cardiac valves and the four heart chambers, through mathematical equations of the underlying physical processes. To study the effect of COVID-19, we varied the heart rate, the contractility and the pulmonary resistances in suitable ranges. ResultsOur computations on individuals with both otherwise normal and impaired cardiac functions revealed that COVID-19 worsen cardiac function, as shown by a decrease of some cardiac biomarkers values such as cardiac output and ejection fraction. In the case of existing impaired cardiac function, the presence of COVID-19 lead to values outside the normal ranges. ConclusionsComputational models revealed to be an effective tool to study the effect of COVID-19 on the cardiovascular system. Such effect could be significant for patients with impaired cardiac function. This is especially useful to perform a sensitivity analysis of the hemodynamics for different conditions. CONDENSED ABSTRACTEmerging studies address how COVID-19 infection might impact the cardiovascular system. This relates particularly to the development of myocardial injury, acute coronary syndrome, myocarditis, arrhythmia, and heart failure. Prospective treatment approach is advised for these patients. By the assessment of conventional important biomarkers obtained with new sources as a 0-dimentional computational model, we propose a new study protocol as an effective method to evaluate short-term prognosis. The clinical protocol proposed will help to rapidly identify which patients require intensive monitoring, diagnostic strategy and most adequate therapy.

pathology↗